# A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

arXiv: https://arxiv.org/abs/1511.01868 · HTML: https://mlsanity.com/paper/1511.01868/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [Correcting Exposure Bias for Link Recommendation](https://mlsanity.com/paper/2106.07041/) (2021)
- [Social physics in the age of artificial intelligence](https://mlsanity.com/paper/2603.16900/) (2026)
- [Diversity Preference-Aware Link Recommendation for Online Social Networks](https://mlsanity.com/paper/2205.10689/) (2022)

## Abstract (arXiv)

Link recommendation has attracted significant attentions from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which include "People You May Know" on LinkedIn and "You May Know" on Google+. In academia, link recommendation has been and remains a highly active research area. This paper surveys state-of-the-art link recommendation methods, which can be broadly categorized into learning-based methods and proximity-based methods. We further identify social and economic theories, such as social interaction theory, that underlie these methods and explain from a theoretical perspective why a link recommendation method works. Finally, we propose to extend link recommendation research in several directions that include utility-based link recommendation, diversity of link recommendation, link recommendation from incomplete data, and experimental study of link recommendation.